Data analytics is the discovery, interpretation, and transmission of meaningful patterns of data. In particular, in areas where information is rich, it relies on concurrent applications of statistics, computer programming, and operation research to quantify performance.
Organizations can apply analysis to business data to describe, predict and improve business performance. Specifically, it includes predictive analysis, normative analysis, enterprise decision making, descriptive analysis, cognitive analysis, big data analysis, retail analysis, store selection and inventory management unit optimization, marketing optimization and marketing mix modeling, web analytics, call Analytics forecast analysis, credit risk analysis, fraud analysis, etc. are used in various fields. Because analysis requires extensive calculations (see large data), the algorithms and software used for analysis take advantage of the latest methods of computer science, statistics and mathematics.
Applications of analytics
- Marketing optimization
- People analytics
- Portfolio analytics
- Risk analytics
- Digital analytics
- Security analytics
- Software analytics
In the commercial analytics software industry, the emphasis is placed on solving the challenges of analyzing large amounts of complex datasets. Such data sets are generally called big data. Problems originating from large-scale data were only found in the scientific community, but today large-scale data is a problem for many companies operating transactional systems online, and as a result, a large amount of data can be quickly Collect.
Analysis of unstructured data types is another issue that is drawing attention in the industry. Unstructured data differs from structured data in that its format differs greatly and it can not be stored in a traditional relational database without any major effort on data conversion. Source of unstructured data such as e-mail, word processor document content, PDF, geospatial data, etc. has become a relevant source of business intelligence of enterprises, governments, and universities.
For example, in the UK, the discovery that illegal selling of illegal doctor’s notes to help deceive employers and insurance companies is an opportunity for insurers to raise vigilance on unstructured data analysis. The McKinsey Global Institute estimates that the US healthcare system will be $ 300 billion annually and the European public sector will be 250 billion euros through extensive data analysis.
These challenges are the contemporary inspiration for many innovations in modern analytical information systems, creating relatively new machine analysis concepts such as complex event processing, full-text search and analysis, and new ideas for presentations. One such innovation is the introduction of a grid-like architecture in mechanical analysis, improving the speed of massively parallel processing by distributing the workload to multiple computers equally accessing the complete data set You can do.
Analytics is increasingly used in education, especially at the district and government level. However, the complexity of students’ performance measurement is a challenge for educators to understand and use the analysis, to distinguish student performance patterns, to predict the possibility of graduation, to increase the likelihood of student success will be presented. 48% of the teachers were difficult to raise questions of data, 36% did not understand the data given, 52% misinterpreted the data. In order to counter this, analytical tools for educators incorporate in-store data formats (labels, supplementary documents, help systems, to improve the understanding and use of educators, package/display and content important The decision will be made) analysis will be displayed.
One new challenge is dynamic regulatory needs. For example, in the banking industry, it is highly likely that small risk banks will adopt internal risk models due to the necessity of Basel III and future capital. In such cases, cloud computing and the open source programming language R help small banks to adopt risk analysis and apply forecast analysis to support branch-level monitoring